Glasses' makeup: the simple and the combined effect of color and shape on perceived volume and beverage intake

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and EconomicsIn order to understand the context of beverages’ intake, it is crucial to bear in mind that there are a wide number of environmental cues which affect both the frequency and the volume ingested by consumers (Wansink, 2004). The horizontal-vertical illusion and the size-contrast illusion are the main causes to the biases regarding the amount of beverage consumed, inasmuch it is known that consumers use heuristics to make area and volume assessments (Krider, Raghubir and Krishna, 2001; Raghubir and Krishna, 1999). Hence, it is relevant to consider cues such as the shape and the size of packages, containers, (Folkes and Matta, 2004; Krider, Raghubir and Krishna, 2001; Raghubir and Krishna, 1999; Wansink and Park, 2001; Wansink and Ittersum, 2003; Wansink, 1996; Wansink, Van Ittersum and Painter, 2006) in what regards to their impact on both perceived and actual consumption. However, the simple and combined effect of color and shape on perceived consumption and intake via the effect of the vertical-horizontal illusion on the perceived amount of beverage has been disregarded in the past. The results of the experiment conducted showed that glasses’ elongation positively influences the perceived volume, while indirectly and inversely affects perceived consumption, the amount of sparkling water being constant on the experiment. Nevertheless, the experiment failed to show the simple and the combined effects of color and shape on volume perceptions and volume ingested by subjects

Training samples from open data for satellite imagery classification: Using K-means clustering algorithm

To create a land use/land cover (LULC) map from a satellite image, we can follow a supervised classification approach if we know what classes exist in the study area and if we have representative training samples for each class. However, in heterogeneous biophysical environments, the wide range of spectral signatures among LULC classes can bias the classification results. In this study, we generated training samples from the official 2015 Portuguese Land Cover Map (COS). In spite of the viability of this source of information (official reference data), we faced some problems with corrupted data and an unbalanced number of training samples per class. As such, we explored the K-means clustering technique in order to understand whether the data had critical issues and to select the most representative training samples by class for satellite imagery classification. We investigated the potential of this technique for LULC classification in a predominantly rural region characterized by a mixed agro-silvo-pastoral environment, which means there is a broad range of spectral signatures for each LULC class. Two image classifications for 2015 were performed using the random forest classifier. The first was done by using the most representative training samples selected from the statistical analysis, and the other was done by using the full generated training set (original training set). Ultimately, the present study demonstrates the improvements in overall accuracy between both image classifications (+8%), showing that the applied methodology has a positive impact on the results.info:eu-repo/semantics/publishedVersio

Long-Term Satellite Image Time-Series for Land Use/Land Cover Change Detection Using Refined Open Source Data in a Rural Region

The increasing availability and volume of remote sensing data, such as Landsat satellite images, have allowed the multidimensional analysis of land use/land cover (LULC) changes. However, the performance of image classification is highly dependent on the quality and quantity of the training set and its temporal continuity, which may a ect the accuracy of the classification and bias the analysis of the LULC changes. In this study, we intended to apply a long-term LULC analysis in a rural region based on a Landsat time series of 21 years (1995 to 2015). Here, we investigated the use of open LULC source data to provide training samples and the application of the K-means clustering technique to refine the broad range of spectral signatures for each LULC class. Experiments were conducted on a predominantly rural region characterized by a mixed agro-silvo-pastoral environment. The open source data of the o cial Portuguese LULC map (Carta de Uso e Ocupação do Solo, COS) from 1995, 2007, 2010, and 2015 were integrated to generate the training samples for the entire period of analysis. The time series was computed from Landsat data based on the normalized di erence vegetation index and normalized di erence water index, using 221 Landsat images. The Time-Weighted Dynamic Time Warping (TWDTW) classifier was used, since it accounts for LULC-type seasonality and has already achieved promising overall accuracy values for classifications based on time series. The results revealed that the proposed method was e cient in classifying a long-term satellite time-series with an overall accuracy of 76%, providing insights into the main LULC changes that occurred over 21 years.info:eu-repo/semantics/publishedVersio

Trends in High Nature Value Farmland and Ecosystem Services Valuation: A Bibliometric Review

High Nature Value farmland (HNVf) represents a rural landscape characterized by extensive farming practices. These lands not only deliver vital ecosystem services (ES) but also serve as significant harbors of biodiversity, underscoring their critical conservation status. Consequently, European Union countries have prioritized the identification, monitoring, and enhancement of HNVf systems in their policies. As governments and international organizations increasingly lean on green subsidies to promote sustainable environmental practices, the valuation of ecosystem services (VES) emerges as a crucial tool. This valuation offers both an economic rationale for conservation and aids in determining the optimal allocation of these subsidies for maximum environmental and economic return on investment. Given the potential for such valuations to shape and justify conservation subsidies, there is a growing imperative to understand the research trends and knowledge gaps in this realm. This article, through a bibliometric review, seeks to illuminate the size, growth trajectory, and thematic tendencies within HNVf and VES literature. Bibliometric analysis is recognized as promising in identifying research trends; thus, this article consists of a bibliometric review of HNVf and VES research. The objective is to identify the size, growth trajectory, and geographic distribution of HNVf and VES literature between the first publication until 2022, while assessing the critical publishing journals, authors, documents, and conceptual structure of the research fields (e.g., economic, social, and environmental). The analysis revealed a predominant concentration of research on HNVf in Europe, with limited studies conducted outside this continent. The primary focus of these studies revolved around subject areas such as environmental science, agriculture, and biological sciences. Conversely, regarding research on VES, there was no clear regional concentration. VES research publications mainly covered the interdisciplinary fields of economics, biology, and policymaking. As the fields of HNVf and VES have evolved, it is evident that there has been a stronger push towards data-driven approaches, emphasizing the need for tangible assessments and precise understanding. In examining the overlap between topics, the analysis revealed a gap between methodologies for HNVf monitoring and conservation and VES, highlighting the need for further development in crafting an integrated approach encompassing both areas.info:eu-repo/semantics/publishedVersio

Tratamento de Dados Open source para Classificação de Imagens de Satélite

A elaboração, com elevada precisão, de mapas de uso e ocupação do solo, através de imagens de satélite e métodos de classificação supervisionados depende, em grande medida, das amostras. Neste sentido, a disponibilidade de informação aberta e grátis oficial é particularmente relevante, uma vez que possibilita um conhecimento mais aprofundado sobre a paisagem do local em estudo e, também, a aplicação de classificações supervisionadas. Todavia, em ambientes biofísicos de elevada heterogeneidade, a ampla gama de assinaturas espectrais e a pequena variação destas entre as classes de uso e ocupação do solo poderá influenciar, de forma menos positiva, a produção destes mapas (Viana et al., 2019). Além desta questão existem, também, problemas relacionados com a informação de base utilizada para gerar as amostras de treino. No presente estudo, a carta de uso e ocupação do solo portuguesa (COS) de 2015 consistiu na informação de base para elaboração das amostras de treino. Desta forma, considerando que na COS as classes são representadas por polígonos que incluem elementos que, na verdade, não correspondem à classe propriamente dita (i.e. estradas de terra batida em torno de campos de cultivo), torna-se particularmente importante a aplicação de metodologias que permitam, a priori, analisar as amostras e, se necessário, proceder ao tratamento destas. Deste modo, é explorada a técnica de classificação por grupos (k-means) em ambiente R com recurso ao tclust package, no sentido de analisar e selecionar as amostras mais representativas de cada classe a classificar (Cuesta-Albertos et al.,1997; Fritz, García-Escudero & Mayo- Iscar, 2012). O presente estudo investiga o potencial desta técnica nas classificações supervisionadas de imagens de satélite numa região predominantemente rural caracterizada por uma mistura de ambientes agro-silvo-pastoris. Assim, realizou-se duas classificações para 2015: i) com as amostras originais ii) com as amostras selecionadas. Por fim, as experiências realizadas resultaram numa melhoria da precisão da classificação, (8%) demonstrando, assim, que a metodologia aplicada evidenciou um impacto positivo nos resultados.info:eu-repo/semantics/publishedVersio

Padrões espaciais da neve durante o verão nas áreas livres de gelo das Ilhas Shetlands do Sul (Antártida)

Os neveiros são mantos de neve, com área e espessura variáveis, que ocorrem em áreas não glaciadas e que perduram, habitualmente, durante o verão austral. Estas acumulações de neve resultam da conjugação de diversos fatores topoclimáticos e são uma importante fonte para a compreensão e análise das alterações climáticas e da sua influência na dinâmica geomorfológica (Christiansen, 1997). O clima no arquipélago das ilhas Shetland do Sul, onde a temperatura média anual ao nível do mar ronda os -2oC e, no verão as médias mensais são positivas e a precipitação é frequente, favorece a fusão estival da neve nas áreas mais baixas. É nas áreas livres de gelo, que se observam de Dezembro a Abril, numerosos neveiros, sendo que alguns se mantêm durante todo o ano. A investigação efetuada na última década na Antártida Marítima demonstrou que os neveiros são fatores importantes para a dinâmica dos ecossistemas terrestres (Bockheim et al. 2013, Guglielmin et al. 2014, Goyanes et al. 2014, Vieira et al. 2014). O objetivo deste trabalho consiste na identificação, quantificação e análise da influência dos fatores topoclimáticos na ocorrência e distribuição da neve durante o verão. Os procedimentos necessários para a concretização deste objetivo assentaram, numa fase inicial, na análise e classificação de imagens de satélite de alta resolução das penínsulas de Fildes, Barton e Weaver (Ilha King George), Byers e Hurd (ilha Livingston) e da Ilha Deception. A primeira etapa, referente à classificação das imagens de satélite, consistiu na identificação e posterior classificação de neve e na avaliação da precisão dessa mesma classificação, através de testes estatísticos. Numa segunda etapa aplicaram-se técnicas de análise espacial utilizando variáveis modeladas a partir dos modelos digitais de elevação (i.e. altimetria, declive, curvatura, radiação solar e exposição ao vento). A determinação da importância das diferentes variáveis foi assegurada pela aplicação de testes estatísticos. Esta metodologia permitiu construir modelos espaciais, recorrendo à regressão logística e ao valor informativo, com o desiderato de mapear a suscetibilidade à ocorrência de neveiros em cada uma das áreas de pormenor. Os resultados finais consistem nos mapas e modelos de suscetibilidade à de neveiros para cada local que permitem, com a sua elaboração, a identificação dos fatores que condicionam a sua ocorrência. A análise dos dados estatísticos e dos resultados dos modelos de suscetibilidade destacaram a altimetria (altitude elevada), declives (moderados a acentuados) e a exposição das vertentes (sul e sudeste) como as variáveis que, independentemente da área pormenor e do método, demonstraram maior influência na manutenção da neve no solo. O peso das restantes varia consoante o método e, sobretudo, a área pormenor. Os resultados dos métodos estatísticos, obtidos nas taxas de sucesso e predição, revelam-se próximos e bastante satisfatórios para todas as áreas pormenor à exceção da Península Barton, Weaver e ilha Deception.Snowpatches are late lying mantles of snow cover occurring outside glacier areas and that are frequently pervasive during the summer season. They normally occur as a result of various topoclimatic factors (e.g. concavities, lee effects, shadow effects, etc.) and their occurrence provides an important source of information regarding climate change and geomorphological processes induced by snow (Christiansen, 1997). The climate of the South Shetlands archipelago, with mean annual air temperatures of -2oC at sea-level and summers showing mean monthly temperatures above freezing, with frequent rainfalls, favours the development of snow free areas normally at low altitude, where snow patches are frequent from December to April, many of them showing a perennial presence. Research conducted in the previous decade in the Maritime Antarctic has shown that snow patches are very important factors for the natural system dynamics and especially for permafrost (e.g. Bockheim et al. 2013, Guglielmin et al. 2014, Goyanes et al. 2014, Vieira et al. 2014). Given this framework, the objective is identifying, quantifying and analysing the influence of topoclimatic factors on the occurrence and distribution of snow in the summer season. The research is based on the classification and analysis of high resolution multispectral remote sensing imagery from Fildes and Barton Peninsula (King George Island), Hurd and Byers Peninsula (Livingston Island) and Deception Island. The imagery is classified for snow patch detection and different classification algorithms are tested for accuracy. The second step consists on applying spatial analysis techniques using variables derived from digital elevation models (i.e. altimetry, slope, aspect, curvature, hillshade, global radiation). The influence of the different variables is assessed through exploratory statistical analysis and discussed according to local factors of each ice-free area and snow melt conditions preceding image acquisition. This approach allows the development of spatial modelling (e.g. logistical regression and informative value) aiming at mapping the probability of occurrence of snowpatches. The analysis of statistical data and the results of susceptibility models highlighted the altitude (high altitude), slopes (moderate to high) and exposure of the slopes (south and southeast) as the variables that regardless of the study area and method, demonstrated greater influence in maintaining snow on the ground. The weight of the remaining variables varies depending on the method and especially on the study area. The results of statistical methods obtained in the success and prediction rates, are close and satisfactory for all areas, except for Barton Peninsula, Weaver and Deception Island

Actinobacteria Isolated From Laminaria ochroleuca: A Source of New Bioactive Compounds

Nature is the major reservoir of biologically active molecules. The urgent need of finding novel molecules for pharmaceutical application is prompting the research of underexplored environments, such as marine ecosystems. Here, we investigated cultivable actinobacteria associated with the macroalgae Laminaria ochroleuca and assessed their potential to produce compounds with antimicrobial or anticancer activities. A specimen of L. ochroleuca was collected in a rocky shore in northern Portugal, and fragments of tissues from different parts of the macroalgae (holdfast, stipe, and blades) were surface sterilized and plated in three culture media selective for actinobacteria. A total of 90 actinobacterial strains were isolated, most of which affiliated with the genus Streptomyces. Isolates associated with the genera Isoptericola, Rhodococcus, Nonomuraeae, Nocardiopsis, Microbispora, and Microbacterium were also obtained. Organic extracts from the isolates were tested for their antimicrobial activity using the agar-based disk diffusion method, followed by determination of minimum inhibitory concentration (MIC) values. Forty-five isolates inhibited the growth of Candida albicans and/or Staphylococcus aureus, with MIC values ranging from <0.5 to 1000 μg mL−1. The actinobacterial isolates were also tested for their anticancer potential on two human cancer cell lines. Twenty-eight extracts affected the viability of at least one human cancer cell line (breast carcinoma T-47D and neuroblastoma SH-SY5Y) and non-carcinogenic endothelial cell line (hCMEC/D3). Seven extracts affected the viability of cancer cells only. This study revealed that L. ochroleuca is a rich source of actinobacteria with promising antimicrobial and anticancer activities and suggests that macroalgae may be a valuable source of actinobacteria and, consequently, of new molecules with biotechnological importance

A Review of the Challenges of Using Deep Learning Algorithms to Support Decision-Making in Agricultural Activities

Deep Learning has been successfully applied to image recognition, speech recognition, and natural language processing in recent years. Therefore, there has been an incentive to apply it in other fields as well. The field of agriculture is one of the most important fields in which the application of deep learning still needs to be explored, as it has a direct impact on human well-being. In particular, there is a need to explore how deep learning models can be used as a tool for optimal planting, land use, yield improvement, production/disease/pest control, and other activities. The vast amount of data received from sensors in smart farms makes it possible to use deep learning as a model for decision-making in this field. In agriculture, no two environments are exactly alike, which makes testing, validating, and successfully implementing such technologies much more complex than in most other industries. This paper reviews some recent scientific developments in the field of deep learning that have been applied to agriculture, and highlights some challenges and potential solutions using deep learning algorithms in agriculture. The results in this paper indicate that by employing new methods from deep learning, higher performance in terms of accuracy and lower inference time can be achieved, and the models can be made useful in real-world applications. Finally, some opportunities for future research in this area are suggested.This work is supported by the R&D Project BioDAgro—Sistema operacional inteligente de informação e suporte á decisão em AgroBiodiversidade, project PD20-00011, promoted by Fundação La Caixa and Fundação para a Ciência e a Tecnologia, taking place at the C-MAST-Centre for Mechanical and Aerospace Sciences and Technology, Department of Electromechanical Engineering of the University of Beira Interior, Covilhã, Portugal.info:eu-repo/semantics/publishedVersio

Performance of the Bethesda system for reporting thyroid cytology in multi-institutional large cohort of pediatric thyroid nodules: a detailed analysis

Background: To evaluate the performance of TBSRTC through multi-institutional experience in the paediatric population and questioning the management recommendation of ATA Guidelines Task Force on Paediatric Thyroid Cancer; Methods: A retrospective search was conducted in 4 institutions to identify consecutive thyroid FNAC cases in paediatric population between 2000 and 2018. Following the 2nd TBSRTC, the risk of malignancy ratios (ROMs) was given in ranges and calculated by 2 different ways. Sensitivity, specificity, PPV, NPV and DA ratios were calculated using histologic diagnosis as the gold standard; Results: Among a total of 405 specimens, the distribution of cases for each category was, 44 (11%) for ND, 204 (50%) for B category, 40 (10%) for AUS/FLUS, 36 (9%) for FN/SFN, 24 (6%) for SFM and 57 (14%) for M categories. 153 cases have a histological diagnosis. The ratio of surgery was 23% in ND, 16% in the B, 45% for AUS/FLUS, 75% for SFN/FN and 92% for SFM and 75% in M categories; Conclusions: The data underlines the high ROM values in paediatric population which might be clinically meaningful. The high rate of malignancy of the cohort of operated patients (50%) also underlines the need of better preoperative indicators for stratification. Considering that more than half of the nodules in AUS/FLUS category were benign, direct surgery recommendation could be questionable as proposed in ATA 2015 guidelines.info:eu-repo/semantics/publishedVersio